Seeking Truth and Beauty in Flavor Physics with Machine Learning
This addresses the problem for theoretical physicists in model discovery, but it is incremental as it applies existing machine learning techniques to a specific domain.
The paper tackled the challenge of developing theoretical physics models that fit experimental data while meeting abstract criteria like beauty and naturalness, by designing loss functions for machine learning optimization, resulting in models that achieve both truth and beauty in the Yukawa quark sector as a toy example.
The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' criteria like beauty, naturalness, etc. We design loss functions for performing both of those tasks with machine learning techniques. We use the Yukawa quark sector as a toy example to demonstrate that the optimization of these loss functions results in true and beautiful models.